3D-stacked many-core architecture for biological sequence analysis problems

Sequence analysis plays critical role in bioinformatics, and most applications of which have compute intensive kernels consuming over 70% of total execution time. By exploiting the compute intensive execution stages of popular sequence analysis applications, we present and evaluate a VLSI architecture with a focus on those that target at biological sequences directly, including pairwise alignment, multiple sequence alignment, database search, and short read sequence mappings. Based on coarse grained reconfigurable array (CGRA) we propose the use of many-core and 3D-stacked technologies to gain further improvement over memory subsystem, which gives another order of magnitude speedup from high bandwidth and low access latency. We analyze our approach in terms of its throughput and efficiency for different application mappings. Initial experimental results are evaluated from a stripped down implementation in a commodity FPGA, and then we scale the results to estimate the performance of our architecture with 9 layers of 68 mm2 stacked wafers in 45-nm process. We demonstrate numerous estimated speedups better than any existed hardware accelerators for at least 39 times for the entire range of applications and datasets of interest. In comparison, the alternative FPGA based accelerators deliver only improvement for single application, while GPGPUs perform not well enough on accelerating program kernel with random memory access and integer addition/comparison operations.